Deep learning with disentangled representations
Over the recent years, deep learning has emerged as a powerful method for learning feature representations from complex input data, and it has been greatly successful in computer vision, speech recognition, and language modeling. The recent successes typically rely on a large amount of supervision (e.g., class labels). While many deep learning algorithms focus on a discriminative task and extract only task-relevant features that are invariant to other factors, complex sensory data is often generated from intricate interaction between underlying factors of variations (for example, pose, morphology and viewpoints for 3d object images). In this work, we tackle the problem of learning deep representations that disentangle underlying factors of variation and allow for complex reasoning and inference that involve multiple factors. Specifically, we develop deep generative models with higher-order interactions among groups of hidden units, where each group learns to encode a distinct factor of variation. We present several successful instances of deep architectures and their learning methods, including supervised and weakly-supervised setting. Our models achieve strong performance in emotion recognition, face verification, data-driven modeling of 3d objects, and video game prediction. I will also present other related ongoing work.
I am an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. I received my Ph.D. from Computer Science Department at Stanford University in 2010, advised by Prof. Andrew Ng. My primary research interests lie in machine learning, which spans over deep learning, unsupervised, semi-supervised, and supervised learning, transfer learning, graphical models, and optimization. I also work on application problems in computer vision, audio recognition, robot perception, and text processing. My work received best paper awards at ICML (2009) and CEAS (2005). I have served as a guest editor of IEEE TPAMI Special Issue on Learning Deep Architectures, as well as area chairs and senior program committee of ICML, NIPS, ICCV, AAAI, IJCAI, and ICLR. I received the Google Faculty Research Award (2011), NSF CAREER Award (2015), and was selected by IEEE Intelligent Systems as one of AI's 10 to Watch (2013).